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Access your health information from any device with MyHealth. You can message your clinic, view lab results, schedule an appointment, and pay your bill. Obesity is a serious, chronic disease that can have a negative effect on many systems in your body. People who are overweight or obese have a much greater risk of developing serious conditions, including:. The U. Surgeon General has declared that obesity has reached epidemic proportions in the United States.
Public health officials warn that the results of physical inactivity and poor diet are catching up to tobacco as a significant threat to health. We are committed to helping you get healthy and stay that way. The causes of obesity are complex. There are many interrelated factors, such as genetics, lifestyle and how your body uses energy. Take the first step to managing your weight from the comfort of your home. Use our BMI calculator to help you determine whether or not you are considered obese.
If you are obese, or have one or more risk factors for obesity, our physicians can help. In cases of severe obesity, surgery may be an option. Learn more about obesity treatments at Stanford. Obesity has a far-ranging negative effect on health.
Each year obesity-related conditions cost over billion dollars and cause an estimated , premature deaths in the US. The health effects associated with obesity include, but are not limited to, the following:. Clinical trials are research studies that evaluate a new medical approach, device, drug, or other treatment. As a Stanford Health Care patient, you may have access to the latest, advanced clinical trials.
Open trials refer to studies currently accepting participants. Closed trials are not currently enrolling, but may open in the future. Obesity Causes Treating Obesity. Share on Facebook. Notice: Users may be experiencing issues with displaying some pages on stanfordhealthcare. We are working closely with our technical teams to resolve the issue as quickly as possible. Generally, studies allow for a nonlinear relationship when modeling the effects of weight on absenteeism by dividing BMI into categories such as under-weight, normal weight, overweight, and obese.
BMI is most often derived from data based on self-reported height and weight. Some studies correct for potential bias under- or over- reporting in data of this kind using correlations between self-reported weight and height and objectively observed values from NHANES.
The outcome variables used also vary in definition across studies. Certain authors, such as Burton et al 25 use only longer periods of health-related work absence, defined as short-term disability, while others use either paid time off for sick leave or self-reported absence due to illness. In order to identify a causal relationship between obesity and absenteeism, authors control for a list of observables that also affect absenteeism; some authors employ econometric models other than standard ordinary least squares OLS regressions in order to control for endogeneity of weight in determining work absence.
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Covariates generally include demographic variables, years of education, income, occupation, smoking or alcohol consumption, and various other health risks or conditions. Frone 26 runs two sets of regressions, the first of which excludes nonweight — related physical and mental health conditions, in order to test whether the addition of those conditions mediates the effect of obesity on absenteeism; he finds that it does.
The result most consistently identified across the studies is a positive and statistically significant correlation between obesity and measures of absenteeism, even after controlling for the covariates discussed above. Because of the differences in methodologies, the magnitudes of the parameter estimates on obesity are not widely comparable. A subset of the authors discussing absenteeism translates their results on the correlation between obesity and absenteeism into dollar amounts representing the cost of the estimated productivity loss. This is usually done by calculating the level of compensation for the relevant workers either from survey data or BLS averages.
This amount includes only the direct productivity costs of absenteeism that the employee is paid while not at work ; it does not account for any secondary effects on training, morale, or other network effects. Obesity could also contribute to productivity loss if obese individuals are less productive while present at the workplace. This may occur as a result of physical and mental health conditions that are more common among obese workers and negatively affect productive ability.
Alternatively, a common outside factor may make individuals more likely to both be obese and relatively less productive. The studies reviewed here focus primarily on the magnitude of the presenteeism effect, rather than the mechanism of action. Studies by Ricci and Chee 31 and Pronk et al 15 both include measures of presenteeism in addition to absenteeism.
Ricci and Chee use the Caremark American Productivity Audit, a phone interview that included several questions regarding health-related reduced work performance. Respondents were asked to estimate the average amount of time elapsed between arriving and starting work on days when they were not feeling well, as well as total hours of lost concentration, repeating a job, or feeling fatigued. The authors then look at total lost productive time LPT the sum of absenteeism and presenteeism , and measure the effects of obesity controlling for a list of covariates.
In a second stage, the authors add a variable for the number of co-occurring health conditions to test whether the effects of obesity are mediated by overall health status. Ricci and Chee find that obese workers are more likely to have positive LPT than their counterparts, and on average have more of it. As also found by Frone, 26 this effect appears to be largely driven by the higher propensity of obese workers to have co-occurring conditions. Of the total cost of LPT, two-thirds is attributable to presenteeism and one-third to absenteeism.
This finding suggests that while more studies have focused on the costs of absenteeism, presenteeism may present a larger problem in terms of dollars lost. Additional work is needed to clarify the relative magnitudes of these costs. Pronk et al 15 include outcome variables that measure quality of work performed as well as workplace inter-personal relationships. The only statistically significant presenteeism relationship found with obesity was on inter-personal relationships. However, the study includes physical activity and cardiorespiratory fitness measures as explanatory variables, which are likely to mediate effects of obesity, as shown in other studies.
In addition to absenteeism and presenteeism, obesity may lead to an increase in disability payments and disability insurance premiums. Such an increase could reflect a loss in productivity beyond what is captured in absenteeism data if recipients are unable to hold a job altogether. Additionally, an increase in the disability rolls represents higher fiscal costs to the federal government. Burkhauser and Cawley 32 study the effects of obesity both on self-reported work impairment and Social Security Disability Insurance. Potential bias introduced by self-reporting of weight is corrected for.
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Results are robust to specification changes for receipt of disability income. For men in the NLSY, being obese raises the probability of receiving disability income by 6. For women, the increased probability of receiving disability is 5. Thus, even after controlling for a list of covariates and endogeneity of weight, the authors find a significant and large effect of obesity on receipt of disability insurance.
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More research is needed to determine the productivity loss associated with this correlation: to what extent does being on disability decrease employment among recipients? Another form of productivity loss associated with obesity is premature mortality or reduction in QALYs. Several studies have found a connection between obesity and mortality. The authors determine the distribution of individuals across BMI categories, as well as life expectancy at each age between 18 and 85 years in each BMI category, and calculate years of life lost YLL in each category relative to a reference BMI of 24 the high end of the normal-weight range.
Effects for black men and women were much smaller. Groessel et al 34 consider the effects of BMI on quality of life in a longitudinal cohort study of older individuals mean age 72 years. After controlling for age, sex, smoking and exercise, they compare statistical differences in mean QWB scores between obese and nonobese BMI groups. Obese individuals were found to have 0. This result is equivalent to one QALY lost for every 20 people who live one year with obesity.
Both premature mortality and lost QALYs represent important economic impacts of obesity. Further research would be needed to monetize this impact for comparison with other costs. Though few studies have considered it, another potential economic cost of obesity is a health insurance market externality. Several studies have estimated the portion of health care expenditure on obesity that is paid for by public insurance. Such a problem could induce additional costs of obesity via welfare loss. The authors note that even if an individual does not consciously choose to consume more calories or exercise less, pooled insurance reduces the price of obesity, and obesity has been shown to be somewhat responsive to price signals eg, food prices.
In order to determine whether there is a welfare loss caused by this externality, the authors consider two models of health insurance: one in which there is complete, employer-provided, pooled insurance, and another in which premiums are risk adjusted. The difference in utility under the optimal solution in each model is then measured to find welfare loss.
After calibrating the model using data from the MEPS, the authors find that there is in fact a welfare loss under pooled insurance.
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The loss is proportional to the product of the difference in medical expenditures between the obese and nonobese, and the elasticity of body weight to the insurance subsidy provided by pooled insurance. Several papers have estimated the total economic cost of obesity, differentiating only between direct and indirect costs. Direct costs include those discussed in the first section of this paper, while indirect costs focus on premature mortality, higher disability insurance premiums, and labor market productivity.
Notably, the papers reviewed here provide a reasonably wide range of estimates for the total indirect costs of obesity. However, direct comparison of results across studies is difficult due to such factors as the date of measurement, representativeness of the sample, and scope of measurement.
Differences in findings may be due to a confluence of factors in the design of the studies, rather than simply differences in econometric specifications or data sources. For example, Thompson et al 36 look at the total cost of obesity to US businesses, differentiating between health insurance expenditures and paid sick leave, life insurance, and disability insurance. On the other hand, a study by Lightwood et al 22 looks at current and future costs of adolescent overweight.
In this case, the indirect costs include work loss due to sick and disability leave, as well as long-term disability, early retirement, and premature mortality.